We evaluate the link prediction on Open Graph Benchmark (OGB) data [1]}.
We use four OGB datasets in experiments: OGBL-DDI, OGBL-COLLAB, OGBL-CITATION2, and OGBL-PPA, which each has a different type of graph.
OGBL-DDI collects the drug-drug interactions as a homogeneous, unweighted, undirected graph, where each node represents a drug.
Drug interactions are represented by edges.
OGBL-COLLAB is an undirected graph, where all nodes come with 128-dimensional features.
In OGBL-COLLAB, each node represents an author and edges indicate the collaboration between authors.
OGBL-CITATION2 is a directed graph, representing the citation network between papers.
A paper with 128-dimensional word2vec characteristics makes up each node.
OGBL-PPA is an undirected and unweighted graph, where every node represents a protein from 58 different species.
The edges in OGBL-PPA indicate biologically meaningful associations between proteins.
The statistics of these datasets is presented in Table. REF .
